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政大機構典藏 > 資訊學院 > 資訊科學系 > 期刊論文 > Item 140.119/143300

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/143300

Title:	Image Impulse Noise Removal Using Cascaded Filtering Based on Overlapped Adaptive Gaussian Smoothing and Convolutional Refinement Networks
Authors:	彭彥璁 Peng, Yan-Tsung Huang, Sha-Wo
Contributors:	資科系
Keywords:	Denoising;cascaded filtering;adaptive Gaussian filtering;convolutional refinement networks
Date:	2021-10
Issue Date:	2023-02-06 14:30:30 (UTC+8)
Abstract:	Impulse noise is often introduced to images when captured through image sensors due to sharp and sudden disturbances in the image signal, analog-to-digital converter errors, sensor temperature, etc., severely degrading their visual quality. Therefore, it is essential to develop an effective method to remove image noise. We propose a novel image denoising method for “salt-and-pepper” (SP) noise, using cascaded filtering based on overlapped adaptive Gaussian smoothing (OAGS) and the convolutional refinement networks (CRNs). First, the noisy input image can be preliminarily denoised by OAGS, where the noisy pixels are removed and recovered. The CRNs refine the result by restoring fine details for the denoised image. Through extensive experimental results, we demonstrate the proposed method substantially outperforms other state-of-the-art denoising methods, especially for high-density SP noise.
Relation:	IEEE Open Journal of the Computer Society, Vol.2, pp.382-392
Data Type:	article
DOI 連結:	https://doi.org/10.1109/OJCS.2021.3117738
DOI:	10.1109/OJCS.2021.3117738
Appears in Collections:	[資訊科學系] 期刊論文

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